Line Profile Measure as a Stopping Criterion in CG-SENSE Algorithm

Conjugate gradient SENSE (CG-SENSE) is a parallel magnetic resonance imaging reconstruction algorithm which solves the inversion problem of SENSE iteratively. One major limitation of CG-SENSE is the appropriate choice of the number of iterations required for good reconstruction results. Fewer iterations result in aliasing artifacts and too many iterations result in an increased noise level. This paper proposes a novel method to define the stopping criterion of CG-SENSE algorithm which is based on the use of correlation measure between the line profiles of the reconstructed images in the current and the previous iterations. The results are compared with Bregman distance-stopping criterion. Artifact power and peak signal-to-noise ratio are used to quantify the quality of the reconstructed images. The results demonstrate that the line profile correlation measure acts as an effective stopping criterion in CG-SENSE.     

以MH2(M=Ti,Zr)为原料反应烧结制备MB2-SiC复相陶瓷

分别以TiH2,ZrH2为原料,结合原位反应与脉冲电流辅助烧结制备了TiB2-SiC及ZrB2-SiC复相陶瓷.研究发现,所制备的复相陶瓷表现出一定的织构化现象,TiB2及ZrB2晶粒在反应烧结过程中其(001)面沿垂直压力和电流方向生长.金属氢化物粉体的粒径大小对复相陶瓷的致密化及微结构有较大影响:粒径越小越有利于陶瓷的致密化和硼化物晶粒的定向生长.由于金属硼化物的定向,复相陶瓷的机械性能表现出各向异性.TiB2-SiC复相陶瓷具有较高的断裂韧性,最高可达7.3 MPa·m1/2,而ZrB2-SiC复相陶瓷具有更高的抗弯强度(937 MPa).     

Automatic detection and quantification of tree-in-bud (TIB) opacities from CT scans

This study presents a novel computer-assisted detection (CAD) system for automatically detecting and precisely quantifying abnormal nodular branching opacities in chest computed tomography (CT), termed tree-in-bud (TIB) opacities by radiology literature. The developed CAD system in this study is based on 1) fast localization of candidate imaging patterns using local scale information of the images, and 2) M?bius invariant feature extraction method based on learned local shape and texture properties of TIB patterns. For fast localization of candidate imaging patterns, we use ball-scale filtering and, based on the observation of the pattern of interest, a suitable scale selection is used to retain only small size patterns. Once candidate abnormality patterns are identified, we extract proposed shape features from regions where at least one candidate pattern occupies. The comparative evaluation of the proposed method with commonly used CAD methods is presented with a dataset of 60 chest CTs (laboratory confirmed 39 viral bronchiolitis human parainfluenza CTs and 21 normal chest CTs). The quantitative results are presented as the area under the receiver operator characteristics curves and a computer score (volume affected by TIB) provided as an output of the CAD system. In addition, a visual grading scheme is applied to the patient data by three well-trained radiologists. Interobserver and observer-computer agreements are obtained by the relevant statistical methods over different lung zones. Experimental results demonstrate that the proposed CAD system can achieve high detection rates with an overall accuracy of 90.96%. Moreover, correlations of observer-observer (R(2)=0.8848, and observer-CAD agreements (R(2)=0.824, validate the feasibility of the use of the proposed CAD system in detecting and quantifying TIB patterns.     

Syntheses and characterization of lisinopril-coated gold nanoparticles as highly stable targeted CT contrast agents in cardiovascular diseases

Lisinopril was used as the targeting moiety to prepare gold nanoparticle-based functional CT contrast agents. Pure lisinopril, thioctic acid-lisinopril conjugate, and reduced thioctic acid-lisinopril conjugate were used to obtain GNP-Lis, GNP-TA-Lis, and GNP-RTA-Lis, respectively, via ligand exchange reaction on citrate-coated gold nanoparticles (GNPs). These lisinopril-decorated GNPs were fully characterized, and their chemical stabilities in biological relevant media and in high salt concentration were compared. Their relative stabilities toward lyophilization and against cyanide-induced decomposition were also investigated. Because of their higher stability, GNP-TA-Lis were used to assess the targeting of angiotensin converting enzyme (ACE) using X-ray computed tomography (CT). The images obtained displayed high contrast in the region of the lungs and heart, clearly indicating the targeting of ACE, whose overexpression is associated with development of cardiac and pulmonary fibrosis. Thus, the new nanoprobes prepared here will serve as very useful tools for the monitoring of cardiovascular pathophysiologies using CT imaging.     

Optimum vibration absorber (tuned mass damper) design for linear damped systems subjected to random loads

Optimum design of dynamic vibration absorbers (DVAs) installed on linear damped systems that are subjected to random loads is studied and closed-form design formulas are provided. Three cases are considered in the optimization process: Minimizing the variance of the displacement, velocity and acceleration of the main mass. Exact optimum design parameters for the velocity case, which to the best knowledge of the author do not exist in the literature, are derived for the first time. Exact solutions are found to be directly applicable for practical use with no simplification needed. For displacement and acceleration cases, a solution for the optimum absorber frequency ratio is obtained as a function of optimum absorber damping ratio. Numerical simulations indicate that optimum absorber damping ratio is not significantly related to the structural damping, especially when the displacement variance is minimized. Therefore, optimum damping ratio derived for undamped systems is proposed for damped systems for the displacement case. When acceleration variance is minimized, however, the optimum damping ratio derived for undamped systems is found not as accurate for damped systems. Therefore, a more accurate approximate expression is derived. Numerical comparisons with published approximate expressions at the same level of complexity indicated that proposed design formula yield more accurate estimates. Another important finding of the paper is that for specific applications where all of the response parameters are desired to be minimized simultaneously, DVAs designed per velocity criteria provide the best overall performance with the least complexity in the design equations.     

Deriving traffic demands for operational IP networks: methodologyand experience

Engineering a large IP backbone network without an accurate network-wide view of the traffic demands is challenging. Shifts in user behavior, changes in routing policies, and failures of network elements can result in significant (and sudden) fluctuations in load. We present a model of traffic demands to support traffic engineering and performance debugging of large Internet service provider networks. By defining a traffic demand as a volume of load originating from an ingress link and destined to a set of egress links, we can capture and predict how routing affects the traffic traveling between domains. To infer the traffic demands, we propose a measurement methodology that combines flow-level measurements collected at all ingress links with reachability information about all egress links. We discuss how to cope with situations where practical considerations limit the amount and quality of the necessary data. Specifically, we show how to infer interdomain traffic demands using measurements collected at a smaller number of edge links-the peering links connecting to neighboring providers. We report on our experiences in deriving the traffic demands in the AT&T IP Backbone, by collecting, validating, and joining very large and diverse sets of usage, configuration, and routing data over extended periods of time. The paper concludes with a preliminary analysis of the observed dynamics of the traffic demands and a discussion of the practical implications for traffic engineering     

NetScope: traffic engineering for IP networks

Managing large IP networks requires an understanding of the current traffic flows, routing policies, and network configuration. However, the state of the art for managing IP networks involves manual configuration of each IP router, and traffic engineering based on limited measurements. The networking industry is sorely lacking in software systems that a large Internet service provider can use to support traffic measurement and network modeling, the underpinnings of effective traffic engineering. This article describes the AT&T Labs NetScope, a unified set of software tools for managing the performance of IP backbone networks. The key idea behind NetScope is to generate global views of the network on the basis of configuration and usage data associated with the individual network elements. Having created an appropriate global view, we are able to infer and visualize the networkwide implications of local changes in traffic, configuration, and control. Using NetScope, a network provider can experiment with changes in network configuration in a simulated environment rather than the operational network. In addition, the tool provides a sound framework for additional modules for network optimization and performance debugging. We demonstrate the capabilities of the tool through an example traffic engineering exercise of locating a heavily loaded link, identifying which traffic demands flow on the link, and changing the configuration of intradomain routing to reduce the congestion     

On the vanishing of homology in random Čech complexes

We compute the homology of random ?ech complexes over a homogeneous Poisson process on the d‐dimensional torus, and show that there are, coarsely, two phase transitions. The first transition is analogous to the Erd?s ‐Rényi phase transition, where the ?ech complex becomes connected. The second transition is where all the other homology groups are computed correctly (almost simultaneously). Our calculations also suggest a finer measurement of scales, where there is a further refinement to this picture and separation between different homology groups. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 51, 14–51, 2017     

A search method for optimal control of a flow shop system of traditional machines

We consider a convex and nondifferentiable optimization problem for deterministic flow shop systems in which the arrival times of the jobs are known and jobs are processed in the order they arrive. The decision variables are the service times that are to be set only once before processing the first job, and cannot be altered between processes. The cost objective is the sum of regular costs on job completion times and service costs inversely proportional to the controllable service times. A finite set of subproblems, which can be solved by trust-region methods, are defined and their solutions are related to the optimal solution of the optimization problem under consideration. Exploiting these relationships, we introduce a two-phase search method which converges in a finite number of iterations. A numerical study is held to demonstrate the solution performance of the search method compared to a subgradient method proposed in earlier work.